Novel Corona Virus Prediction and Transmission Analysis using Machine Learning Models

Today we all are suffering from Covid-19, a novel virus and it is the most harmful disease across the world which mainly comes under the domain of health care research. Healthcare system gives importance to health states of the population or individual. Healthcare plays a vital role in promoting physical and mental health and well- being of people around the world. Efficient health care system leads to country's economy, industrialization and development. Corona virus is dangerous animal and human pathogens and it is threatening people by spreading all over the world. Corona virus patients mostly suffer from lung infection studies have shown it clinically. We proposed detailed analysis on how to predict the expected death, recovered and confirmed cases based on the available data across the world using various machine learning models. Especially we constructed linear regression model (LRM), support vector machine model (SVMM) and polynomial regression models (PRM) and predicted future expected cases over a period of next 15 days. The error between the predicted model and official data curve is quite small in the process of transmission in data modeling. Compare to other models Polynomial regression model performs best prediction of corona positive cases. Forward prediction and backward inference of the epidemic helps to take decisions for necessary actions during Covid-19 propagation. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)

CNN Features and Optimized Generative Adversarial Network for COVID-19 Detection from Chest X-Ray Images.

Coronavirus is a RNA type virus, which makes various respiratory infections in both human as well as animals. In addition, it could cause pneumonia in humans. The Coronavirus affected patients has been increasing day to day, due to the wide spread of diseases. As the count of corona affected patients increases, most of the regions are facing the issue of test kit shortage. In order to resolve this issue, the deep learning approach provides a better solution for automatically detecting the COVID-19 disease. In this research, an optimized deep learning approach, named Henry gas water wave optimization-based deep generative adversarial network (HGWWO-Deep GAN) is developed. Here, the HGWWO algorithm is designed by the hybridization of Henry gas solubility optimization (HGSO) and water wave optimization (WWO) algorithm. The pre-processing method is carried out using region of interest (RoI) and median filtering in order to remove the noise from the images. Lung lobe segmentation is carried out using U-net architecture and lung region extraction is done using convolutional neural network (CNN) features. Moreover, the COVID-19 detection is done using Deep GAN trained by the HGWWO algorithm. The experimental result demonstrates that the developed model attained the optimal performance based on the testing accuracy of 0.9169, sensitivity of 0.9328, and specificity of 0.9032.